CN103440589A - Store site selection system and method - Google Patents

Abstract

The invention discloses a store site selection system and a store site selection method. The method comprises the following steps: determining a trading area range according to the business type of a new store, determining existing stores and residential quarters in the trading area and searching attraction influence factor information of the stores; quantizing the environment in the store by using factor analysis; performing multiple linear regression analysis on the amount of sales of a plurality of stores by using a multiple linear regression formula according to the obtained environment in the store and other collected influence factors, determining the influence degree of each influence factor on attraction in the trading area and taking the influence degree as an adjusting index; calculating the attraction of the new store by using a multi-factor attraction model according to the influence factor and the corresponding adjusting index thereof obtained by regression analysis. According to the system and the method, retail enterprises can very conveniently determine the store customer sources and the distribution characteristics according to the store scales, the environments, the service and the like.

Description

Shops's site selection system and method

Technical field

The present invention, about a kind of shops site selection system and method, particularly relates to a kind of shops's site selection system and method based on the multiple services key element.

Background technology

In more and more ripe business environment, much retail shops can be gathered in a commercial circle, has each other competitive relation.The Hough model is external a kind of computing method of often using when retail shop commercial circle scale is investigated, Main Basis sales field gravitation and will usually being analyzed apart from these two of resistances, use the Hough model can obtain the trip probability that removes the particular business facility from residence, the sales volume of prediction commercial facility, set-off ability and the tableization thereof of commercial concentration, thereby learn that commercial circle structure and competitive relation what kind of can occur and change, also often use this model at the investigation big-box retailer during to the influence power of peripheral commercial concentration.Yet Hough and correction model thereof be from the consumer, all just considered the scale of shops and calculated the attractive force of each shops apart from these two kinds of factors:

a wherein _jfor the scale of certain shops, D _ijrepresent the distance of certain zone and shops, λ, μ is for regulating index.

And in fact shops to client's attractive force scale and the distance except shops, also have other many factors, as the competitive power of the kind of shopping atmosphere, service, price and commodity, enterprise, reputation etc.

The cloth Rec is expanded the Hough model, has proposed theoretically multifactorial attractive force model:

The factor that attracts client to come shop to buy comprises the kind of scale, shopping atmosphere, service, price and commodity etc., and hinder the consumer, come the factor of shop shopping to comprise traffic, distance etc., this model is just as a kind of theoretic discussion, can't know which factor is really influential, each factor has the impact of much degree to determine on attractive force in this commercial circle on earth.

Summary of the invention

The deficiency existed for overcoming above-mentioned prior art, the present invention's purpose is to provide a kind of shops site selection system and method, by to Market Site Selection, analysis further quantizes, improving this siting analysis makes the supvr be familiar with Consumer Characteristics in commercial circle, promote the supvr further to improve management and service according to these characteristics, make in commercial circle shops's layout more reasonable, avoid the waste of social resources.

For reaching above-mentioned and other purpose, the present invention proposes a kind of shops site selection system, at least comprises:

The information module, determine the commercial circle scope according to the industry situation of newly running a shop, and determines existing shops and residential quarter in this commercial circle, and collect the attractive force influence factor information of these shops;

In shop, environment quantizes module, utilize factorial analysis to be quantized environment in shop, utilize principal component analysis (PCA) to find out the main factor, obtain the factor score function, and according to the eigenwert of each Main Factors, calculate the ratio of each factor, utilize the ratio of each factor and the score of each factor, environment parameter in the shop of each shops is turned to a numerical value;

The multiple linear regression analysis module, according to environment and other each influence factors of collecting in the shop obtained, utilize a multiple linear regression formula to carry out multiple linear regression analysis to the sales volume of a plurality of shops, analyze the relation of sales volume and each influence factor, determine in this commercial circle that each influence factor is to the attractive force influence degree, and using influence degree as regulating index;

The shops attractive force is calculated module, the influence factor obtained according to regretional analysis and regulate accordingly index and utilize a multifactorial attractive force model to calculate the attractive force that makes new advances and run a shop.

Further, this attractive force influence factor information comprises sales volume, area of business, sale category, parking lot number of units, cashier number, non-production marketing district area, goods return and replacement service, industry situation, each residential quarter Dao Ge shops distance, in this shop for environment parking lot number of units, cashier number, non-production marketing district area mean.

Further, in the shop of each shops, environment parameter turns to the sum of products of each factor score and factor ratio.

Further, this multiple linear regression formula is as follows:

y＝β ₀+β ₁x ₁+β ₂x ₂+Λ+β _nx _n

Wherein y is sales volume, x _ifor each influence factor, each influence factor can be passed through factor beta to client's influence degree ₁, β ₂, Λ β _nobtain.

Further, this multifactorial attractive force model is:

$P_j=\frac{S_j^{\mathrm{\α}}*C_j^{\mathrm{\β}}*E_j^{\mathrm{\δ}}*Q_j^{\mathrm{\λ}}*F_j^{\mathrm{\ρ}}*{\left(\frac{1}{D_j}\right)}^{\mathrm{\θ}}}{{\mathrm{\ΣS}}_j^{\mathrm{\α}}*C_j^{\mathrm{\β}}*E_j^{\mathrm{\δ}}*Q_j^{\mathrm{\λ}}*F_j^{\mathrm{\ρ}}*{\left(\frac{1}{D_j}\right)}^{\mathrm{\θ}}}$

Wherein S represents area of business, and C is category, and E is that in shop, environment, Q represent service quality, and F is industry situation, D is distance, α, β, δ, λ, ρ, θ is the corresponding index of regulating, it is the number of influence factor that the adjusting index has constraint condition alpha+beta+δ+λ+ρ+θ=6.

Further, the adjusting index of arbitrary influence factor is the number that n is influence factor.

For achieving the above object, the present invention also provides a kind of shops site selecting method, comprises the steps:

Determine the commercial circle scope according to the industry situation of newly running a shop, determine existing shops and residential quarter in this commercial circle, and collect the attractive force influence factor information of these shops;

Utilize factorial analysis to be quantized environment in shop, utilize principal component analysis (PCA) to find out the main factor, obtain the factor score function, and according to the eigenwert of each Main Factors, calculate the ratio of each factor, utilize the ratio of each factor and the score of each factor, environment parameter in the shop of each shops is turned to a numerical value;

According to environment and other each influence factors of collecting in the shop obtained, utilize a multiple linear regression formula to carry out multiple linear regression analysis to the sales volume of a plurality of shops, analyze the relation of sales volume and each influence factor, determine in this commercial circle that each influence factor is to the attractive force influence degree, and using influence degree as regulating index;

The influence factor obtained according to regretional analysis and regulate accordingly index and utilize a multifactorial attractive force model to calculate the attractive force that makes new advances and run a shop.

Further, this attractive force influence factor information comprises sales volume, area of business, sale category, parking lot number of units, cashier number, non-production marketing district area, goods return and replacement service, industry situation, each residential quarter Dao Ge shops distance, in this shop for environment parking lot number of units, cashier number, non-production marketing district area mean.

Further, in the shop of each shops, environment parameter turns to the sum of products of each factor score and factor ratio.

Further, this multiple linear regression formula is as follows:

y＝β ₀+β ₁x ₁+β ₂x ₂+Λ+β _nx _n

Wherein y is sales volume, x _ifor each influence factor, each influence factor can be passed through factor beta to client's influence degree ₁, β ₂, Λ β _nobtain.

Further, this multifactorial attractive force model is:

$P_j=\frac{S_j^{\mathrm{\α}}*C_j^{\mathrm{\β}}*E_j^{\mathrm{\δ}}*Q_j^{\mathrm{\λ}}*F_j^{\mathrm{\ρ}}*{\left(\frac{1}{D_j}\right)}^{\mathrm{\θ}}}{{\mathrm{\ΣS}}_j^{\mathrm{\α}}*C_j^{\mathrm{\β}}*E_j^{\mathrm{\δ}}*Q_j^{\mathrm{\λ}}*F_j^{\mathrm{\ρ}}*{\left(\frac{1}{D_j}\right)}^{\mathrm{\θ}}}$

Wherein S represents area of business, and C is category, and E is that in shop, environment, Q represent service quality, and F is industry situation, D is distance, α, β, δ, λ, ρ, θ is the corresponding index of regulating, it is the number of influence factor that the adjusting index has constraint condition alpha+beta+δ+λ+ρ+θ=6.

Further, the adjusting index of arbitrary influence factor is

the number that n is influence factor.

Compared with prior art, the present invention's a kind of shops site selection system and method are by carrying out the factorial analysis quantification to environment in shop, and utilization multiple linear regression analysis, analyze sales volume and scale, kind, environment in shop, service level, the relation of many influence factors such as industry situation and distance, determine in this commercial circle that each factor is to the attractive force influence degree, and using this influence degree as the attractive force of regulating index and calculate each addressing that is subject to multifactor impact, can easily make retailer according to the scale of oneself running a shop, definite client of shops source such as environment and service and distribution characteristics, for retailer's day-to-day operations management, the prediction of retailer's Operating profit and the selection of various strategies and tactics provide definite quantitative analysis and theoretical direction.

The accompanying drawing explanation

The system architecture diagram that Fig. 1 is a kind of shops of the present invention site selection system;

The flow chart of steps that Fig. 2 is a kind of shops of the present invention site selecting method.

Embodiment

Below, by specific instantiation accompanying drawings embodiments of the present invention, those skilled in the art can understand other advantage of the present invention and effect easily by content disclosed in the present specification.The present invention also can be different by other instantiation implemented or applied, the every details in this instructions also can be based on different viewpoints and application, carries out various modifications and change not deviating under spirit of the present invention.

The system architecture diagram that Fig. 1 is a kind of shops of the present invention site selection system.As shown in Figure 1, a kind of shops of the present invention site selection system at least comprises: in information module 101, shop, environment quantizes module 102, multiple linear regression analysis module 103 and shops's attractive force calculating module 104.

Wherein, information module 101 is determined the commercial circle scope according to the industry situation of running a shop, determine existing retail shops and residential quarter in this commercial circle, then collect the attractive force influence factor information of these shops, as information such as sales volume, area of business, sale category, parking lot number of units, cashier number, non-production marketing district area (service, food and drink, amusement etc.), goods return and replacement service, industry situation, each residential quarter Dao Ge shops distances.

In shop, environment quantification module 102 utilizes factorial analysis to be quantized environment in shop, utilize principal component analysis (PCA) to find out the main factor, obtain the factor score function, and according to the eigenwert of each Main Factors, calculate the ratio of each factor, utilize the ratio of each factor and the score of each factor, environment parameter in the shop of each shops is turned to a numerical value.In preferred embodiment of the present invention, each shops environment can mean with parking lot number of units, cashier number, non-production marketing district area (service, food and drink, amusement etc.) etc., but in the shop needed in final siting analysis, environment is a quantitative numerical value, therefore in shop, environment quantification module 102 utilizes factorial analysis to be quantized environment in shop, utilize principal component analysis (PCA) to find out the main factor, obtain following factor score function:

$\left\{\begin{array}{c}{F}_1={\mathrm{\μ}}_{11}{x}_1+{\mathrm{\μ}}_{12}{x}_2+{\mathrm{\μ}}_{13}{x}_3+\mathrm{\Λ}+{\mathrm{\μ}}_{1p}{x}_p\\ F_2={\mathrm{\μ}}_{21}{x}_1+{\mathrm{\μ}}_{22}{x}_2+{\mathrm{\μ}}_{23}{x}_3+\mathrm{\Λ}+{\mathrm{\μ}}_{2p}{x}_p\\ \mathrm{\Λ}\\ F_n={\mathrm{\μ}}_{p1}{x}_1+{\mathrm{\μ}}_{p2}{x}_2+{\mathrm{\μ}}_{p3}{x}_3+\mathrm{\Λ}+{\mathrm{\μ}}_{\mathrm{pp}}{x}_p\end{array}\right.$

Wherein, F _ifor each factor score, μ _j1, μ _j2, Λ, μ _jpthe factor values coefficient between j the factor and original variable, x _ivalue for a certain factor.

This factor score function, be exactly the new coordinate of each shops on the factor in this commercial circle, then according to the eigenwert of each Main Factors, calculates the ratio of each factor, if for example get two Main Factors, their eigenwert is (λ ₁, λ ₂), the ratio of these two factors is respectively

in the shop of each shops, environment just can be quantified as a numerical value, is the Σ of each shops (score of the factor ratio * factor).

Multiple linear regression analysis module 103 utilizes a multiple linear regression formula to carry out multiple linear regression analysis to the sales volume of a plurality of shops according to environment and other each influence factors of collecting in the shop obtained, analyze the relation of many influence factors such as environment, service level, industry situation and distance in sales volume and scale, kind, shop, determine in this commercial circle that each influence factor is to the attractive force influence degree, and using influence degree as regulating index.

Environment, industry situation, distance, service level etc. in the sales volume, area of business, sale category, shop of commercial circle Nei Ge shops have been known, just can carry out multiple linear regression analysis to the sales volume of a plurality of shops accordingly, in preferred embodiment of the present invention, distance is with Σ (community is to shops's distance).Concrete multiple linear regression formula is as follows:

y＝β ₀+β ₁x ₁+β ₂x ₂+Λ+β _nx _n

Wherein y is sales volume, x _ifor each influence factor as environment in area of business, category, shop, service, industry situation and distance etc.Just can know by analyzing in commercial circle, which factor has appreciable impact to the consumer, and which has no significant effect the consumer.Rejecting does not have influential factor, and each factor also can be passed through factor beta to client's influence degree simultaneously ₁, β ₂, Λ β _nobtain.

Shops's attractive force is calculated module 104 according to the influence factor of regretional analysis acquisition and is regulated accordingly index and utilize a multifactor attractive force model to calculate the attractive force that makes new advances and run a shop.

In preferred embodiment of the present invention, in considering commercial circle during the influence degree of a plurality of factors, shops's attractive force computing formula (multifactorial attractive force model) is when considered in area of business, category, shop the influence factor such as environment, service and distance:

$P_j=\frac{S_j^{\mathrm{\α}}*C_j^{\mathrm{\β}}*E_j^{\mathrm{\δ}}*Q_j^{\mathrm{\λ}}*F_j^{\mathrm{\ρ}}*{\left(\frac{1}{D_j}\right)}^{\mathrm{\θ}}}{{\mathrm{\ΣS}}_j^{\mathrm{\α}}*C_j^{\mathrm{\β}}*E_j^{\mathrm{\δ}}*Q_j^{\mathrm{\λ}}*F_j^{\mathrm{\ρ}}*{\left(\frac{1}{D_j}\right)}^{\mathrm{\θ}}}$

Wherein S represents area of business, and C is category, and E is that in shop, environment, Q represent service quality, and F is industry situation, D is distance, α, β, δ, λ, ρ, θ is the corresponding index of regulating, now regulating index, constraint condition alpha+beta+δ+λ+ρ+θ=6 are arranged is the number of influence factor.

Known the influence coefficient of influence factor and each factor from the regretional analysis of multiple linear regression analysis module 103, but now influence coefficient addition needs not be equal to the number of influence factor.In preferred embodiment of the present invention, can be after each influence coefficient normalization, then amplify n doubly, the number that n is influence factor.The adjusting index that is arbitrary influence factor is

Just can be very easily when having known influence factor and having regulated index according to top formula shops attractive force computing formula, calculate the attractive force of newly running a shop.

The flow chart of steps that Fig. 2 is a kind of shops of the present invention site selecting method.As shown in Figure 2, a kind of shops of the present invention site selecting method, comprise the steps:

Step 201, determine the commercial circle scope according to the industry situation of newly running a shop, determine existing retail shops and residential quarter in this commercial circle, then collect the information such as sales volume, area of business, sale category, parking lot number of units, cashier number, non-production marketing district area (service, food and drink, amusement etc.), goods return and replacement service, industry situation, each residential quarter Dao Ge shops distance of these shops.

Step 202, utilize principal component analysis (PCA) to find out the main factor, obtains the factor score function, and according to the eigenwert of each Main Factors, calculate the ratio of each factor, utilize the ratio of each factor and the score of each factor, environment parameter in the shop of each shops is turned to a numerical value.

In preferred embodiment of the present invention, each shops environment can mean with parking lot number of units, cashier number, non-production marketing district area (service, food and drink, amusement etc.) etc., but in the shop needed in final siting analysis, environment is a quantitative numerical value, therefore step 202 utilizes factorial analysis to be quantized environment in shop, utilize principal component analysis (PCA) to find out the main factor, obtain following factor score function:

$\left\{\begin{array}{c}{F}_1={\mathrm{\μ}}_{11}{x}_1+{\mathrm{\μ}}_{12}{x}_2+{\mathrm{\μ}}_{13}{x}_3+\mathrm{\Λ}+{\mathrm{\μ}}_{1p}{x}_p\\ F_2={\mathrm{\μ}}_{21}{x}_1+{\mathrm{\μ}}_{22}{x}_2+{\mathrm{\μ}}_{23}{x}_3+\mathrm{\Λ}+{\mathrm{\μ}}_{2p}{x}_p\\ \mathrm{\Λ}\\ F_n={\mathrm{\μ}}_{p1}{x}_1+{\mathrm{\μ}}_{p2}{x}_2+{\mathrm{\μ}}_{p3}{x}_3+\mathrm{\Λ}+{\mathrm{\μ}}_{\mathrm{pp}}{x}_p\end{array}\right.$

Wherein, F _ifor each factor score, μ _j1, μ _j2, Λ, μ _jpthe factor values coefficient between j the factor and original variable, x _ivalue for a certain factor.

This factor score function, be exactly the new coordinate of each shops on the factor in this commercial circle, then according to the eigenwert of each Main Factors, calculates the ratio of each factor, if for example get two Main Factors, their eigenwert is (λ ₁, λ ₂), the ratio of these two factors is respectively

in the shop of each shops, environment just can be quantified as a numerical value, is the Σ of each shops (score of the factor ratio * factor).

Step 203, according to each influence factor of collecting, utilize a multiple linear regression formula to carry out multiple linear regression analysis to the sales volume of a plurality of shops, analyze the relation of many influence factors such as environment, service level, industry situation and distance in sales volume and scale, kind, shop, determine in this commercial circle that each influence factor is to the attractive force influence degree, and using influence degree as regulating index.

Environment, industry situation, distance, service level etc. in the sales volume, area of business, sale category, shop of commercial circle Nei Ge shops have been known, just can carry out multiple linear regression analysis to the sales volume of a plurality of shops accordingly, in preferred embodiment of the present invention, distance is with Σ (community is to shops's distance).Concrete multiple linear regression formula is as follows:

y＝β ₀+β ₁x ₁+β ₂x ₂+Λ+β _nx _n

Wherein y is sales volume, x _ifor each influence factor as environment in area of business, category, shop, service, industry situation and distance etc.Just can know by analyzing in commercial circle, which factor has appreciable impact to the consumer, and which has no significant effect the consumer.Rejecting does not have influential factor, and each factor also can be passed through factor beta to client's influence degree simultaneously ₁, β ₂, Λ β _nobtain.

Step 204, the influence factor obtained according to regretional analysis and regulate accordingly index and utilize a multifactorial attractive force model to calculate the attractive force that makes new advances and run a shop.

In preferred embodiment of the present invention, in considering commercial circle during the influence degree of a plurality of factors, shops's attractive force computing formula (multifactorial attractive force model) is when considered in area of business, category, shop the influence factor such as environment, service and distance:

$P_j=\frac{S_j^{\mathrm{\α}}*C_j^{\mathrm{\β}}*E_j^{\mathrm{\δ}}*Q_j^{\mathrm{\λ}}*F_j^{\mathrm{\ρ}}*{\left(\frac{1}{D_j}\right)}^{\mathrm{\θ}}}{{\mathrm{\ΣS}}_j^{\mathrm{\α}}*C_j^{\mathrm{\β}}*E_j^{\mathrm{\δ}}*Q_j^{\mathrm{\λ}}*F_j^{\mathrm{\ρ}}*{\left(\frac{1}{D_j}\right)}^{\mathrm{\θ}}}$

Wherein S represents area of business, and C is category, and E is that in shop, environment, Q represent service quality, and F is industry situation, D is distance, α, β, δ, λ, ρ, θ is the corresponding index of regulating, now regulating index, constraint condition alpha+beta+δ+λ+ρ+θ=6 are arranged is the number of influence factor.

Known the influence coefficient of influence factor and each factor from the regretional analysis of multiple linear regression analysis module 103, but now influence coefficient addition needs not be equal to the number of influence factor.In preferred embodiment of the present invention, can be after each influence coefficient normalization, then amplify n doubly, the number that n is influence factor.The adjusting index that is arbitrary influence factor is

Just can be very easily when having known influence factor and having regulated index according to top formula shops attractive force computing formula, calculate the attractive force of newly running a shop.

Visible, in each commercial circle, the consumer has different behavioural characteristics, much retail shops are now distributing in each commercial circle, the present invention can infer consumer behaviour feature in this commercial circle according to shops's sales histories data in this commercial circle, shops attracts the consumer characteristic to comprise scale, kind, environment in shop, service level, industry situation, distance etc., in shop, environment can pass through the cashier number, parking space number, non-production marketing district area etc. carries out the factorial analysis quantification, distance Nei Ge community, available commercial circle is determined to each retail shops Weighted distance, service level is by moving back, the situation of exchanging goods is determined, then use multiple linear regression analysis, analyze sales volume and scale, kind, environment in shop, service level, the multifactorial relations such as industry situation and distance, determine in this commercial circle that each factor is to the attractive force influence degree.Can, using this impact size as regulating index, utilize shops's attractive force computing formula just can accurately calculate very much the attractive force of each addressing that is subject to multifactor impact.

In sum, the present invention's a kind of shops site selection system and method are by carrying out the factorial analysis quantification to environment in shop, and utilization multiple linear regression analysis, analyze sales volume and scale, kind, environment in shop, service level, the relation of many influence factors such as industry situation and distance, determine in this commercial circle that each factor is to the attractive force influence degree, and using this influence degree as the attractive force of regulating index and calculate each addressing that is subject to multifactor impact, can easily make retailer according to the scale of oneself running a shop, definite client of shops source such as environment and service and distribution characteristics, for retailer's day-to-day operations management, the prediction of retailer's Operating profit and the selection of various strategies and tactics provide definite quantitative analysis and theoretical direction, simultaneously, the present invention can make the supvr be familiar with Consumer Characteristics in commercial circle, promote the supvr further to improve management and service according to these characteristics, make in commercial circle shops's layout more reasonable, avoid the waste of social resources.

Above-described embodiment is illustrative principle of the present invention and effect thereof only, but not for limiting the present invention.Any those skilled in the art all can, under spirit of the present invention and category, modify and change above-described embodiment.Therefore, the scope of the present invention, should be as listed as claims.

Claims (10)

1. shops's site selection system at least comprises:

The information module, determine the commercial circle scope according to the industry situation of newly running a shop, and determines existing shops and residential quarter in this commercial circle, and collect the attractive force influence factor information of these shops;

In shop, environment quantizes module, utilize factorial analysis to be quantized environment in shop, utilize principal component analysis (PCA) to find out the main factor, obtain the factor score function, and according to the eigenwert of each Main Factors, calculate the ratio of each factor, utilize the ratio of each factor and the score of each factor, environment parameter in the shop of each shops is turned to a numerical value;

The multiple linear regression analysis module, according to environment and other each influence factors of collecting in the shop obtained, utilize a multiple linear regression formula to carry out multiple linear regression analysis to the sales volume of a plurality of shops, analyze the relation of sales volume and each influence factor, determine in this commercial circle that each influence factor is to the attractive force influence degree, and using influence degree as regulating index;

The shops attractive force is calculated module, the influence factor obtained according to regretional analysis and regulate accordingly index and utilize a multifactorial attractive force model to calculate the attractive force that makes new advances and run a shop;

This attractive force influence factor information comprises sales volume, area of business, sale category, parking lot number of units, cashier number, non-production marketing district area, goods return and replacement service, industry situation, each residential quarter Dao Ge shops distance, in this shop for environment parking lot number of units, cashier number, non-production marketing district area mean.

2. a kind of shops as claimed in claim 1 site selection system, is characterized in that,

In the shop of each shops, environment parameter turns to the sum of products of each factor score and factor ratio;

This multiple linear regression formula is as follows:

y＝β ₀+β ₁x ₁+β ₂x ₂+Λ+β _nx _n

Wherein y is sales volume, x _ifor each influence factor, each influence factor can be passed through factor beta to client's influence degree ₁, β ₂, Λ β _nobtain.

3. a kind of shops as claimed in claim 2 site selection system, is characterized in that, this multifactorial attractive force model is:

$P_j=\frac{S_j^{\mathrm{\α}}*C_j^{\mathrm{\β}}*E_j^{\mathrm{\δ}}*Q_j^{\mathrm{\λ}}*F_j^{\mathrm{\ρ}}*{\left(\frac{1}{D_j}\right)}^{\mathrm{\θ}}}{{\mathrm{\ΣS}}_j^{\mathrm{\α}}*C_j^{\mathrm{\β}}*E_j^{\mathrm{\δ}}*Q_j^{\mathrm{\λ}}*F_j^{\mathrm{\ρ}}*{\left(\frac{1}{D_j}\right)}^{\mathrm{\θ}}}$

Wherein S represents area of business, and C is category, and E is that in shop, environment, Q represent service quality, and F is industry situation, D is distance, α, β, δ, λ, ρ, θ is the corresponding index of regulating, it is the number of influence factor that the adjusting index has constraint condition alpha+beta+δ+λ+ρ+θ=6.

4. a kind of shops as claimed in claim 3 site selection system, it is characterized in that: the adjusting index of arbitrary influence factor is the number that n is influence factor.

5. shops's site selecting method, comprise the steps:

Determine the commercial circle scope according to the industry situation of newly running a shop, determine existing shops and residential quarter in this commercial circle, and collect the attractive force influence factor information of those shops;

Utilize factorial analysis to be quantized environment in shop, utilize principal component analysis (PCA) to find out the main factor, obtain the factor score function, and according to the eigenwert of each Main Factors, calculate the ratio of each factor, utilize the ratio of each factor and the score of each factor, environment parameter in the shop of each shops is turned to a numerical value;

According to environment and other each influence factors of collecting in the shop obtained, utilize a multiple linear regression formula to carry out multiple linear regression analysis to the sales volume of a plurality of shops, analyze the relation of sales volume and each influence factor, determine in this commercial circle that each influence factor is to the attractive force influence degree, and using influence degree as regulating index;

The influence factor obtained according to regretional analysis and regulate accordingly index and utilize a multifactor attractive force model to calculate the attractive force that makes new advances and run a shop.

6. a kind of shops as claimed in claim 5 site selecting method, it is characterized in that: this attractive force influence factor information comprises sales volume, area of business, sale category, parking lot number of units, cashier number, non-production marketing district area, goods return and replacement service, industry situation, each residential quarter Dao Ge shops distance, in this shop for environment parking lot number of units, cashier number, non-production marketing district area mean.

7. a kind of shops as claimed in claim 6 site selecting method, it is characterized in that: in the shop of each shops, environment parameter turns to the sum of products of each factor score and factor ratio.

8. a kind of shops as claimed in claim 7 site selecting method, is characterized in that, this multiple linear regression formula is as follows:

y＝β ₀+β ₁x ₁+β ₂x ₂+Λ+β _nx _n

Wherein y is sales volume, x _ifor each influence factor, each influence factor can be passed through factor beta to client's influence degree ₁, β ₂, Λ β _nobtain.

9. a kind of shops as claimed in claim 8 site selecting method, is characterized in that, this multifactorial attractive force model is:

$P_j=\frac{S_j^{\mathrm{\α}}*C_j^{\mathrm{\β}}*E_j^{\mathrm{\δ}}*Q_j^{\mathrm{\λ}}*F_j^{\mathrm{\ρ}}*{\left(\frac{1}{D_j}\right)}^{\mathrm{\θ}}}{{\mathrm{\ΣS}}_j^{\mathrm{\α}}*C_j^{\mathrm{\β}}*E_j^{\mathrm{\δ}}*Q_j^{\mathrm{\λ}}*F_j^{\mathrm{\ρ}}*{\left(\frac{1}{D_j}\right)}^{\mathrm{\θ}}}$

Wherein S represents area of business, and C is category, and E is that in shop, environment, Q represent service quality, and F is industry situation, D is distance, α, β, δ, λ, ρ, θ is the corresponding index of regulating, it is the number of influence factor that the adjusting index has constraint condition alpha+beta+δ+λ+ρ+θ=6.

10. a kind of shops as claimed in claim 9 site selecting method, it is characterized in that: the adjusting index of arbitrary influence factor is

the number that n is influence factor.

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